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Maximizing Performance: From Compiler Optimization to AI-Powered Cloud Efficiency

A comprehensive look at enhancing system efficiency highlights diverse approaches, from foundational compiler techniques to advanced AI-driven cloud management. One exploration delves into the Integer Set Library (ISL), a critical C library for polyhedral optimization. This detailed primer explains ISL's core concepts, data structures, and API for analyzing and optimizing loop nests and memory layouts, essential for sophisticated compiler optimization and code generation. Complementing this, original research from Google unveils LAVA, an innovative AI-powered system revolutionizing cloud data center resource allocation. LAVA continuously re-predicts virtual machine lifetimes and employs algorithms like NILAS, LAVA, and LARS to optimize VM placement, minimize fragmentation, and reduce migrations. In production, NILAS has significantly improved resource efficiency, marking a new standard for cloud operations.

calendar_today 2025-10-19 attribution www.jeremykun.com/

Integer Set Library (ISL) - A Primer

Dive deep into the Integer Set Library (ISL), a foundational C library for polyhedral optimization that empowers compilers to precisely analyze and optimize loop nests. This primer demystifies ISL's core concepts, from representing complex integer sets and relations with quasi-affine Presburger formulas to manipulating them via its powerful C API. It covers ISL's data structures, constraint matrix representation including 'div' variables for non-linear operations, and practical examples like generating C loop nests and composing memory layout relations. The article also touches upon ISL's interoperability with MLIR's Fast Presburger Library (FPL), making it essential for technical professionals interested in compiler optimization and code generation.
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calendar_today 2025-10-17 attribution research.google/blog/

Solving virtual machine puzzles: How AI is optimizing cloud computing

Cloud data centers grapple with a complex Tetris-like puzzle, efficiently allocating virtual machines (VMs) with unknown lifespans. Google Research introduces LAVA, an AI-powered system that revolutionizes this by continuously repredicting VM lifetimes using probability distributions. LAVA's algorithms (NILAS, LAVA, LARS) optimize VM placement, minimize fragmentation, and reduce migrations, significantly improving resource efficiency. Directly compiled into the scheduler for ultra-low latency, NILAS in production increased empty hosts by 2.3-9.2 percentage points and reduced resource stranding, setting a new standard for cloud optimization.
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